Motion detection is one of the most important procedures in computer vision based surveillance system. Recently, there are lots of related researches about motion detection, such as frame difference and background subtraction. In this paper, we propose an efficient method which uses 2+1D wavelet transformation on video streams to obtain features in spatial and temporal domains. Feature combination and connected component labeling are then performed for moving regions detection. The detected moving region are incorrect due to regions of support of wavelet bases, so we present a location calibration method to conquer this problem, and use minimum bounding rectangles to label the calibrated detecting regions. Finally, we refine the minimum bounding rectangles by an iterative algorithm and adopt a pyramid method to reduce the fragmentation of objects. Our method is verified by experiments. The experimental videos include indoor and outdoor scenes with pedestrians, planar road and expressway with moving vehicles. Our system can find precise object position even when severe shadow effect exists. It is demonstrated by experiments that our method is robust to shadows, sudden light change, and shaking of background or camera.